“Multilevel” structure:

“Multilevel” models:

  • Multilevel models developed in 1980s in demography (Entwistle), statistics (Wong/Mason), education (Bryk/Raudenbush).
  • Use depends critically on theory.

The development of multi-level approaches in epidemiologic research may facilitate research which elucidates the independent and joint effects of individual and environmental factors on health behaviors and health outcomes.

Epidemiology has lost its way

Social context and ‘population perspective’ has been forgotten.


And needs to refocus on “environments”


Our work with individuals has been useful and productive, but this approach alone clearly will not lead to an effective program of health promotion and disease prevention. A new inititive focusing on the environments in which we live much now become a priority for us all.

Multilevel health determinants

Social context a crucial element of conceptual models for ‘social determinants of health’


Why multilevel social epidemiology?

  • Place-based comparisons of health are revealing (Villermé, Farr, Graunt, Snow, DuBois, many others)

  • Communities inherently reflect social dynamics.

  • Host-Agent-Environment (physical and social).

  • “Population perspective”, contra biomedical individualism.

John Snow’s ‘Grand Experiment’. See Snow (1855) reprinted (1936)

Early influential studies in social epidemiology


Neighborhood ‘effects’ on violence, mortality

  • Focus on mutual adjustment
  • Clustering addressed as nuisance

Extended to CVD, low birthweight, other outcomes

  • Random effects implementation
  • Exploration of multi-level EMM

  • Strong theory, field measurements, sophisticated models, potential mechanisms linked to violent crime.

Multilevel analyses showed that a measure of collective efficacy yields a high between-neighborhood reliability and is negatively associated with variations in violence, when individual-level characteristics, measurement error, and prior violence are controlled. Associations of concentrated disadvantage and residential instability with violence are largely mediated by collective efficacy.

Focus on ‘simultaneous’ effects:

By incorporating multiple levels of determination in the study of individual outcomes, multilevel analysis allows for the effects of macro- and micro-level variables as well as their interactions

Potential:

Multilevel analysis is one way to begin to restore a population or societal dimension to epidemiologic research

The ‘Big Idea’:

The big idea is that what matters in determining mortality and health in a society is less the overall wealth of that society and more how evenly wealth is distributed.

  • Inequality = contextual, but how?

State of the Evidence: 2001

  • 25 studies but only 10 used multilevel models, however…

In 23 of the 25 studies we identified, researchers reported a statistically significant association between at least one neighbourhood measure of socioeconomic status and health, controlling for individual socioeconomic status.

  • Potential for intervention:

…serve the purpose of identifying types of geographical areas where traditional public health interventions, aimed at individual risk reduction, may best be targeted.

Traditional measures of association such as odds ratios thus provide an incomplete epidemiological basis for decision making in public health interventions.

Large-scale ‘multilevel’ RCT

  • ~4600 families in high poverty randomized to housing vouchers.

  • Generated large differences in exposure to high-poverty neighborhoods.

  • 5-year follow-up (2003):

    • No impacts on economic self-sufficiency of mothers.
    • Other outcomes mixed, some positive, some negative.
  • Many limitations.

A skeptical view

The recent and enthusiastic adoption of the multilevel model for neighborhood effects research appears to be a case of statisticism, a term used to describe an almost ritualistic appeal to significance testing and both sampling and measurement error when they are not the problem

A skeptical view

The recent and enthusiastic adoption of the multilevel model for neighborhood effects research appears to be a case of statisticism, a term used to describe an almost ritualistic appeal to significance testing and both sampling and measurement error when they are not the problem

What are the problems?

  • Social stratification
  • Endogeneity
  • Extrapolation
  • Spillovers

Income inequality: not so bad for health?


  • Evidence for the income inequality/health link was “slowly dissipating”

  • Multilevel studies inconsistent in US.

  • Weak evidence from Europe and Asia.

  • Individual-level controls matter.

Fixed effects: No.

Random effects: Yes!

Zombie hypothesis…

Neighborhood evidence to 2007

  • 86 multilevel papers on neighborhoods
  • 80% cross-sectional designs
  • Inconsistencies within and across studies.

Neighborhood effects at 20 years

“it is not clear how much we are learning, or whether such lessons are improving population health…experimental evidence of neighborhood effects is mixed, and observational studies too often report mere correlations, side-stepping critical effect identification issues. Since epidemiologists have long known that disadvantaged environments are not healthy, the utility of studies that do not face the difficult methodological challenges is questionable”

More of the same?

  • Now > 250 papers
  • Most still using cross-sectional designs with Census data.
  • Emphasized the importance of timing of exposures over the lifecourse.
  • Pleas for more diversity in study designs.

Merging of multilevel and causal inference

  • Greater focus on credible study designs.

    • Cluster RCTs
    • Quasi-experiments
  • Utilizing longitudinal data to focus on changes in exposure

  • Weighting methods to deal with observables and post-exposure covariates

  • Extensions to mediation


All fit within the scope of multilevel design and analysis

Methods development and clarification

  • Defining assumptions for causal effects of contextual exposures

  • Time-varying exposures and confounding

  • Conditional vs. marginal effects

Nandi and Kawachi (2011)

“Fixing” neighborhood research?

These findings provide little support for social causation as the explanation for associations between neighborhood characteristics and health outcomes.

Healthy discussion of MTO design / results

Healthy discussion of MTO design / results


…the ITT estimate…can successfully measure the effects of the policy initiative, but is not well suited to capturing neighborhood effects.

  • Assessed duration of exposure to neighborhood conditions
  • Find benefits of shorter exposure to low-poverty

Healthy discussion of MTO design / results


Random assignment of families to different MTO mobility groups…generates large differences in average neighborhood trajectories

Nonexperimental analyses of the type conducted by CM reintroduce all of the selection bias problems that MTO was designed to overcome.

Observational data as a neighborhood experiment

  • Time-varying covariates controlled using IPTW, exposure effects estimated using MSMs.

  • Can replicate MTO findings.

  • Found significant lagged effect of living in concentrated disadvantage compared with advantage at wave 1

Lifecourse ‘lens’ on MTO

  • Moving when young increases college attendance and earnings
  • Moving as an adolescent has slightly negative impacts.

… suggests that the duration of exposure to better environments during childhood is an important determinant


…incoming refugees were assigned to neighborhoods with varying levels of disadvantage throughout the country

…As a result, this study attempts to address the challenges of selective migration present in existing studies on neighborhood outcomes.


…refugees who were assigned to more disadvantaged neighborhoods were more likely to develop hypertension, hyperlipidemia, diabetes, and MI in subsequent decades.

Effect sizes were small, representing a 2% increase from baseline rates for each condition…

  • Recent review of ‘causal analyses’ of neighborhood effects.

  • Much more mixed.

  • Evidence of selection and confounding.

  • Lots of heterogeneity.

  • Stronger evidence for children than adults.

Community RCTs are (often) MLMs!

New approaches to measurement

  • Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)
  • Individuals nested within social strata
  • Partial pooling of intersectoral identities
  • Can reveal heterogeneity beyond simple additive effects.

\[y_{ij} = \beta \gamma_{j} + \mu_{0j} + e_{0ij}\]

\[[\mu_{0j}] \sim N(0, \sigma^{2}_{strata})]\]

\[[e_{0ij}] \sim N(0, \sigma^{2}_{e_{0}})\]

  • MAIHDA allows a deeper look at multi-dimensional heterogeneity.

Summary: What have we learned?


Multilevel models

  • Helped to push social epi forward.
  • Perhaps short of promises.
  • More cross-sectional random effects designs unlikely to help.

Neighborhood effects

  • Heterogeneous but reliably negative associations between adverse neighborhood conditions and health.
  • Particularly for children with longer exposure.
  • Potential underutilization of cluster-randomized interventions.

References

1996. BMJ 312, 0–0.
Anderson, R.T., Sorlie, P., Backlund, E., Johnson, N., Kaplan, G.A., 1997. Epidemiology 8, 42–47.
Arcaya, M.C., Tucker-Seeley, R.D., Kim, R., Schnake-Mahl, A., So, M., Subramanian, S.V., 2016. Social Science & Medicine 168, 16–29.
Cerdá, M., Diez-Roux, A.V., Tchetgen Tchetgen, E., Gordon-Larsen, P., Kiefe, C., 2010. Epidemiology 21, 482–489.
Chetty, R., Hendren, N., Katz, L.F., 2016. American Economic Review 106, 855–902.
Chyn, E., Katz, L.F., 2023. NBER Working Paper.
Clampet‐Lundquist, S., Massey, D.S., 2008. American Journal of Sociology 114, 107–143.
Congdon, P., 1997. European Journal of Population 13, 305–338.
Dahlgren, G., Whitehead, M., 1991. Policies and strategies to promote social equity in health. Institute for Future, Stockholm, Sweden.
Diderichsen, F., Hallqvist, J., 1998. Inequality in health—a Swedish perspective. Stockholm: Swedish Council for Social Research 25–39.
Diez-Roux, A.V., 1998. Am J Public Health 88, 216–222.
Diez-Roux, A.V., Nieto, F.J., Muntaner, C., Tyroler, H.A., Comstock, G.W., Shahar, E., Cooper, L.S., Watson, R.L., Szklo, M., 1997. American Journal of Epidemiology 146, 48–63.
Dunn, J.R., Park, G.-R., Brydon, R., Veall, M., Rolheiser, L.A., Wolfson, M., Siddiqi, A., Ross, N.A., 2024. J Epidemiol Community Health.
Ecob, R., 1996. Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, 61.
Evans, C.R., Williams, D.R., Onnela, J.-P., Subramanian, S.V., 2018. Social Science & Medicine 203, 64–73.
Galster, G., Sharkey, P., 2017. RSF: The Russell Sage Foundation Journal of the Social Sciences 3, 1–33.
Glymour, M.M., Mujahid, M., Wu, Q., White, K., Tchetgen Tchetgen, E.J., 2010. Annals of Epidemiology 20, 856–861.
Hamad, R., Öztürk, B., Foverskov, E., Pedersen, L., Sørensen, H.T., Bøtker, H.E., White, J.S., 2020. JAMA Netw Open 3, e2014196.
Hong, G., Raudenbush, S.W., 2008. Journal of Educational and Behavioral Statistics 33, 333–362.
Hubbard, A.E., Ahern, J., Fleischer, N.L., Laan, M.V.D., Lippman, S.A., Jewell, N., Bruckner, T., Satariano, W.A., 2010. Epidemiology 21, 467–474.
Jokela, M., 2014. American Journal of Epidemiology 180, 776–784.
Kaplan, G.A., Pamuk, E.R., Lynch, J.W., Cohen, R.D., Balfour, J.L., 1996. BMJ 312, 999–1003.
Kennedy, B.P., Kawachi, I., Prothrow-Stith, D., 1996. BMJ 312, 1004–1007.
Krieger, N., 1994. Social Science & Medicine 39, 887–903.
Ludwig, J., Liebman, J.B., Kling, J.R., Duncan, G.J., Katz, L.F., Kessler, R.C., Sanbonmatsu, L., 2008. American Journal of Sociology 114, 144–188.
Lynch, J., Smith, G.D., Harper, S., Hillemeier, M., Ross, N., Kaplan, G.A., Wolfson, M., 2004. Milbank Q 82, 5–99.
Mackenbach, J.P., 2002. BMJ 324, 1–2.
McKinlay, J.B., Marceau, L.D., 1999. Am J Public Health 89, 295–298.
Mellor, J.M., Milyo, J., 2003. Health Serv Res 38, 137–151.
Merlo, J., 2003. Journal of Epidemiology & Community Health 57, 550–552.
Merlo, J., 2005a. Journal of Epidemiology & Community Health 59, 443–449.
Merlo, J., 2005b. Journal of Epidemiology & Community Health 59, 729–736.
Merlo, J., 2006. Journal of Epidemiology & Community Health 60, 290–297.
Merlo, J., 2018. Social Science & Medicine 203, 74–80.
Moyer, R., MacDonald, J.M., Ridgeway, G., Branas, C.C., 2019. Am J Public Health 109, 140–144.
Nandi, A., Kawachi, I., 2011. Neighborhood Effects on Mortality, in: Rogers, R.G., Crimmins, E.M. (Eds.), International Handbook of Adult Mortality. Springer Netherlands, Dordrecht, pp. 413–439.
O’Campo, P., Gielen, A.C., Faden, R.R., Xue, X., Kass, N., Wang, M.C., 1995. Am J Public Health 85, 1092–1097.
O’Campo, P., Xue, X., Wang, M.C., Caughy, M., 1997. Am J Public Health 87, 1113–1118.
Oakes, J.M., 2004. Social Science & Medicine 58, 1969–1971.
Oakes, J.M., Andrade, K.E., Biyoow, I.M., Cowan, L.T., 2015. Curr Epidemiol Rep 2, 80–87.
Pearce, N., 1996. Am J Public Health 86, 678–683.
Persmark, A., Wemrell, M., Evans, C.R., Subramanian, S.V., Leckie, G., Merlo, J., 2020. Critical Public Health 30, 398–414.
Pickett, K.E., 2001. Journal of Epidemiology & Community Health 55, 111–122.
Riva, M., Gauvin, L., Barnett, T.A., 2007. Journal of Epidemiology & Community Health 61, 853–861.
Sampson, R.J., 2008. American Journal of Sociology 114, 189–231.
Sampson, R.J., Raudenbush, S.W., Earls, F., 1997. Science 277, 918–924.
Sampson, R.J., Sharkey, P., Raudenbush, S.W., 2008. Proc. Natl. Acad. Sci. U.S.A. 105, 845–852.
Schwartz, S., 1994. Am J Public Health 84, 819–824.
Snow, J., Frost, W.H., Richardson, B.W., 1936. Snow on cholera: Being a reprint of two papers. Commonwealth Fund, New York.
South, E.C., MacDonald, J.M., Tam, V.W., Ridgeway, G., Branas, C.C., 2023. JAMA Internal Medicine 183, 31–39.
Subramanian, S.V., Blakely, T., Kawachi, I., 2003a. Health Serv Res 38, 153–167.
Subramanian, S.V., Jones, K., Duncan, C., 2003b. Multilevel Methods for Public Health Research, in: Kawachi, I., Berkman, L.F. (Eds.), Neighborhoods and Health. Oxford University Press, p. 0.
Subramanian, S.V., O’Malley, A.J., 2010. Epidemiology 21, 475–478.
Susser, M., Susser, E., 1996. Am J Public Health 86, 674–677.
Syme, S.L., 1994. Daedalus 123, 79–86.
Von Korff, M., Koepsell, T., Curry, S., Diehr, P., 1992. American Journal of Epidemiology 135, 1077–1082.